Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 30521
Image Processing Using Color and Object Information for Wireless Capsule Endoscopy

Authors: Gilwon Yoon, Yong-Gyu Lee, Jin-Hee Park

Abstract:

Wireless capsule endoscopy provides real-time images in the digestive tract. Capsule images are usually low resolution and are diverse images due to travel through various regions of human body. Color information has been a primary reference in predicting abnormalities such as bleeding. Often color is not sufficient for this purpose. In this study, we took morphological shapes into account as additional, but important criterion. First, we processed gastric images in order to indentify various objects in the image. Then, we analyzed color information in the object. In this way, we could remove unnecessary information and increase the accuracy. Compared to our previous investigations, we could handle images of various degrees of brightness and improve our diagnostic algorithm.

Keywords: Image Processing, object identification, capsule endoscopy, HSV model, Color Separation

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1073126

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1595

References:


[1] A. Mata, J. Llach, and JM. Bordas, “Wireless Capsule Endoscopy,” World J Gastroenterol, April 7, 2008, Vol. 14, no.13, pp. 1969 -1971.
[2] A. Glukhovsky, “Wireless capsule endoscopy”, Sensor Review, vol. 23, no. 2, 2003, pp. 128–133.
[3] D. G. Adler and C. J. Gostout, “Wireless Capsule Endoscopy”, Hospital Physician, 2003, pp.14-22.
[4] Y.-G. Lee, and G. Yoon, “Bleeding Detection Algorithm for Capsule Endoscopy,” World academy of science engineering and technology, vol. 81, pp. 672-677, Sem., 2011.
[5] Y.-G. Lee, and G. Yoon, “Real-Time Image Analysis of Capsule Endoscopy for Bleeding Discrimination in Embedded System Platform,” World academy of science engineering and technology, vol. 59, pp. 2526-2530, Dec., 2011.
[6] Y.-G. Lee, and G. Yoon, “Improvement of Blood Detection Accuracy using Image Processing Techniques suitable for Capsule Endoscopy,” World academy of science engineering and technology, vol. 65, pp. 1096-1099, May, 2012.
[7] Tony Lindeberg, “Edge Detection and Ridge Detection with Automatic Scale Selection,” International J of Computer Vision, Nov, 1998, vol. 30, no. 2, pp. 117-156.
[8] Heath, M. D., Sarkar, S., Sanocki, T., Bowyer, K. W., “A robust visual method for assessing the relative performance of edge-detection algorithms,” IEEE Trans on Pattern Analysis and Machine Intelligence, vol. 19, no. 12, pp. 1338-1359, Dec, 1997.
[9] F Catté, PL Lions, JM Morel, T Coll, “Image Selective Smoothing and Edge Detection by Nonlinear Diffusion,” SIAM Journal on Numerical Analysis, vol. 29, no.1, pp. 182-193, Feb, 1992
[10] JH Elder, SW Zucker, “Local scale control for edge detection and blur estimation,” IEEE Trans on Pattern Analysis and Machine Intelligence, vol. 20, no. 7, pp. 699-715, Jul, 1998.
[11] J. F. Canny, “A Computational Approach to Edge Detection,” IEEE Trans. Pattern Analysis and Machine intelligence, vol. PAMI-8, no. 6, pp. 679–698, Nov. 1986.